Toward an Organizational Science of Multi-Agent LLM Systems: Decoupling Who, How, and Which Algorithm

2026-07-28

AI Summary

This work addresses the tight coupling among organizational structure, coordination mechanisms, and collaboration algorithms in multi-agent large language model systems, which hinders independent configuration and evaluation. To resolve this, the paper introduces IMACS, a novel framework that formalizes classical organizational theories—specifically Belbin team roles, Mintzberg’s coordination mechanisms, and the RACI responsibility model—into configurable components, thereby achieving orthogonal decoupling across three layers: team composition, agent alignment, and collaboration algorithms. The framework unifies six collaboration protocols under a common interface and incorporates a context-aware multi-armed bandit-based adaptive routing mechanism for dynamic, task-driven protocol selection. Experimental results demonstrate that adaptive routing significantly outperforms fixed protocols, with optimal configurations varying across model families, thus validating both the necessity of decoupled design and the efficacy of online learning.

Abstract

Multi-agent frameworks built on large language models (LLMs) routinely entangle three logically distinct concerns: who is on the team (organization), how members align (coordination), and which algorithm fuses their work (collaboration protocol). IMACS (Intelligent Multi-Agent Collaboration System) separates the three into orthogonal, independently swappable layers. Classic organizational theory (Belbin roles, Mintzberg coordination, RACI accountability) becomes executable, validated configuration, and the framework places six published collaboration algorithms behind a common interface while exposing roles, coordination, and accountability as independently configurable factors. We use this separation to conduct controlled comparisons in which organizational assignments vary while the collaboration protocol is held fixed. It also turns protocol choice into a variable that can be learned: Adaptive Org Routing, a contextual-bandit meta-protocol, selects a protocol per task under an explicit quality-cost tradeoff, outperforms every fixed protocol in a controlled study, and trains online on real benchmark and LLM-judge rewards. The ablations expose a mechanism. Accountability placement changes outcomes exactly when the protocol routes the deliverable through the accountable agent, and the winning placement flips across model families, so organizational design cannot be hard-coded; it must be revalidated, or learned, for each model binding.

Problem

• multi-agent systems
• large language models
• organizational design
• coordination
• collaboration protocol

Innovation

• multi-agent LLM systems
• organizational decoupling
• collaboration protocol
• adaptive routing
• accountability placement